Discrete time inverse optimal neural control: Application for a planar robot

Abstract

This paper presents an inverse optimal neural controller, which is constituted by the combination of two well known techniques: (a) inverse optimal control to avoid solving the Hamilton Jacobi Bellman (HJB) equation associated to nonlinear system optimal control, and (b) an on-line neural identifier, which uses a recurrent neural network, trained with the extended Kalman filter (EKF), in order to build a model of an assumed unknown nonlinear system. The applicability of the proposed approach is illustrated via simulation by the control of a planar robot.